Real-Time Emission Prediction with Detailed Chemistry under Transient Conditions for Hardware-in-the-Loop Simulations

The increasing requirements to further reduce pollutant emissions, particularly with regard to the upcoming Euro 7 (EU7) legislation, cause further technical and economic challenges for the development of internal combustion engines. All the emission reduction technologies lead to an increasing complexity not only of the hardware, but also of the control functions to be deployed in engine control units (ECUs). Virtualization has become a necessity in the development process in order to be able to handle the increasing complexity. The virtual development and calibration of ECUs using hardware-in-the-loop (HiL) systems with accurate engine models is an effective method to achieve cost and quality targets. In particular, the selection of the best-practice engine model to fulfil accuracy and time targets is essential to success. In this context, this paper presents a physically- and chemically-based stochastic reactor model (SRM) with tabulated chemistry for the prediction of engine raw emissions for real-time (RT) applications. First, an efficient approach for a time-optimal parametrization of the models in steady-state conditions is developed. The co-simulation of both engine model domains is then established via a functional mock-up interface (FMI) and deployed to a simulation platform. Finally, the proposed RT platform demonstrates its prediction and extrapolation capabilities in transient driving scenarios. A comparative evaluation with engine test dynamometer and vehicle measurement data from worldwide harmonized light vehicles test cycle (WLTC) and real driving emissions (RDE) tests depicts the accuracy of the platform in terms of fuel consumption (within 4% deviation in the WLTC cycle) as well as NOx and soot emissions (both within 20%).

Model-Based Dynamic Performance Simulation of a Microturbine

Microturbines can be used not only in models and education but also to propel UAVs. However, their wider adoption is limited by their relatively low efficiency and durability. Validated simulation models are required to monitor their performance, improve their lifetime, and design engine control systems. This study aims at developing a numerical model of a micro gas turbine for prediction and prognostics of engine performance. To build a reliable zero-dimensional model, the available compressor and turbine maps were scaled to the available test bench data with the least squares method, to meet the performance of the engine achieved during bench and flight tests. A steady-state aeroengine model was implemented in GSP and compared with experimental operating points. The selected flight data was then used as input for the transient engine model. The exhaust gas temperature (EGT) and fuel flow were chosen as the two key parameters to validate the model, comparing the numerical predicted values with the experimental ones. The observed difference between the model and the flight data was lower than 3% for both EGT and fuel flow.

Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

The choice of users’ activity in a context-aware environment depends on users’ preferences and background. Users tend to rank concurrent activities and select their preferred activity. Researchers and developers of context-aware applications have sought various mechanisms to implement context reasoning engines. Recent implementations use Artificial Neural Networks (ANN) and other machine learning techniques to develop a context-aware reasoning engine to predict users’ activities. However, the complexities of these mechanisms overwhelm the processing capabilities and storage capacity of mobile devices. The study models a context-aware reasoning engine using a multi-layered perceptron with a gradient descent back-propagation algorithm to predict activity from user-ranked activities using a stochastic learning mode with a constant learning rate. The work deduced that working with specific rules in training a neural network is not always applicable. Training a network without approximation of neuron’s output to the nearest whole number increases the accuracy level of the network at the end of the training.

Control-oriented modeling, validation, and interaction analysis of turbocharged lean-burn natural gas variable speed engine

Accurate modeling and control of the gas exchange process in a modern turbocharged spark-ignited engine is critical for the control and analysis of different control strategies. This paper develops a simple physics-based, five-state engine model for a large four-stroke spark-ignited turbocharged engine fueled by natural gas that is used in variable speed applications. The control-oriented model is amenable for control algorithm development and includes the impacts of modulation to any combination of four actuators: throttle valve, bypass valve, fuel rate, and wastegate valve. The control problem requires tracking engine speed to provide propulsive power, differential pressure across the throttle valve to prevent compressor surge, air-to-fuel ratio to restrict engine emissions. Two validation strategies, open-loop and closed-loop, are used to validate the accuracy of both nonlinear and linear versions of the control-oriented model. The control models are able to capture the engine dynamics within 5%–10% error at most of the engine operating points. Finally, the relative gain array (RGA) is applied to the linearized engine model to understand the degree of interactions between plant inputs and outputs as a function of frequency for various operating points. Results of the RGA analysis show that the preferred input-output pairing changes depending on the linear plant model as well as frequency. Therefore, a coordinated controller is ideal to tackle the control problem in question.

Adaptive Fuzzy PID Controller applied to micro turbojet engine

Digital controllers are utilized for controlling modern gas turbine engines. Firstly, an identified discrete model is built for a micro turbojet engine (MTE) jet cat P200sx. Two controllers are designed, gain scheduling PID and adaptive fuzzy tuned PID controllers are presented in this paper. Analysis of traditional PID and Adaptive fuzzy tuned PID controller applied for micro turbojet engine is presented. According to the fuzzy rules, a fuzzy logic controller (FLC) is developed to modify the gains of the PID controller automatically. MATLAB/Simulink is utilized to simulate the complete device consisting of an adaptive fuzzy PID controller, and the micro turbojet engine model. The two controllers’ responses are compared. A comparison of the robustness of each controller against the effect of noise and rejection of disturbance is illustrated. The results showed that, through small rise time, small set time, minimal overshoot, and minimal SteadyState speed error, the PID controller adaptive fuzzy tuned provides better dynamic MTE action and thus superior performance.

Optimal Integrated Emission Management through Variable Engine Calibration

In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.